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Brain. 2019 May 1;142(5):1215-1226. doi: 10.1093/brain/awz063.

Electrophysiological and transcriptomic correlates of neuropathic pain in human dorsal root ganglion neurons.

Author information

1
Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA.
2
The Departments of Anesthesia and Pain Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA, USA.
3
School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas, USA.
4
Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA, USA.
5
The University of Texas Health Science Center, Houston, Texas, USA.
6
Department of Biological Sciences and Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas, USA.

Abstract

Neuropathic pain encompasses a diverse array of clinical entities affecting 7-10% of the population, which is challenging to adequately treat. Several promising therapeutics derived from molecular discoveries in animal models of neuropathic pain have failed to translate following unsuccessful clinical trials suggesting the possibility of important cellular-level and molecular differences between animals and humans. Establishing the extent of potential differences between laboratory animals and humans, through direct study of human tissues and/or cells, is likely important in facilitating translation of preclinical discoveries to meaningful treatments. Patch-clamp electrophysiology and RNA-sequencing was performed on dorsal root ganglia taken from patients with variable presence of radicular/neuropathic pain. Findings establish that spontaneous action potential generation in dorsal root ganglion neurons is associated with radicular/neuropathic pain and radiographic nerve root compression. Transcriptome analysis suggests presence of sex-specific differences and reveals gene modules and signalling pathways in immune response and neuronal plasticity related to radicular/neuropathic pain that may suggest therapeutic avenues and that has the potential to predict neuropathic pain in future cohorts.

KEYWORDS:

DRG transcriptomics; machine learning in healthcare; neuropathy; spontaneous activity

PMID:
30887021
PMCID:
PMC6487328
[Available on 2020-05-01]
DOI:
10.1093/brain/awz063

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